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Published on: December 19, 2020
A Lightweight AMResNet Architecture with an Attention Mechanism for Diagnosing COVID-19
Qi Zhou1, Jamal Alzobair Hammad Kowah1, Huijun Li1
1Medical College, Guangxi University, Nanning 530004, P.R. China.
Aims:
COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. We proposed a lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19, named AMResNet.
Background:
COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered.
Objective:
A lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19.
Method:
By introducing the channel attention mechanism and image spatial information attention mechanism, a better level can be achieved without increasing the number of model parameters.
Result:
In the collected data sets, we achieved an average accuracy rate of more than 92%, and the sensitivity and specificity of specific disease categories were also above 90%.
Conclusion:
The convolution neural network framework can be used as a novel method for artificial intelligence to diagnose COVID-19 or other diseases based on medical images.
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